Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning
Abstract
1. Introduction
2. Methodology
2.1. Data Collection
2.1.1. Study Area
2.1.2. Physical Measurements
2.1.3. Questionnaire Surveys
2.2. ML Model Establishment and Optimization
2.2.1. Data Preprocess
2.2.2. Model Construction and Evaluation
2.2.3. Model Explanation
3. Results
3.1. Model Optimization and Selection
3.2. SHAP Analysis of Feature Impacts
3.2.1. Importance Ranking of Features
3.2.2. Impact of Features: Positive or Negative
4. Discussion
4.1. Comparison in TSV, TA, TCV, and HSV
4.2. Model BO Optimization
4.3. Comparison with Other Studies
4.3.1. Selection of Influencing Factors
4.3.2. Research Metrics and Ranking of Influencing Factors
4.4. Limitations and Future Research
- (1)
- Limitations of Data Collection: This study combines survey responses and physical measurements; however, the survey results may be influenced by participants’ subjective factors, potentially introducing bias. Future research could incorporate more objective measurement tools, such as thermal comfort sensors or physiological data (e.g., skin temperature and heart rate), to complement subjective data. Additionally, optimizing the survey design to reduce subjective bias would improve the reliability of the results.
- (2)
- Limitations of Time Span: The study’s time frame is relatively short, covering only July 2024, which may not fully capture the long-term effects of climate change on thermal comfort. Future research should extend the time span to include multiple seasons or years and consider factors such as air pollution and seasonal variations, providing a more comprehensive analysis of thermal comfort and a deeper understanding of the long-term effects of climate change.
- (3)
- Limitations of Model Selection: This study utilizes supervised learning models for analysis, but the performance of these models may be limited by the algorithms themselves and may vary across different scenarios. Future research could explore additional machine learning algorithms, such as deep learning or ensemble learning, and conduct cross-validation and repeated experiments to improve model stability and generalization. Moreover, incorporating more complex models or deep learning techniques could enhance prediction accuracy and interpretability [76,77,78].
- (4)
- Limitations of Data Coverage: This study primarily focuses on data from the Guangzhou tram stations. Future research should expand to include more cities and regions to enhance the generalizability and applicability of the findings. Cross-regional studies will provide a more comprehensive evaluation of thermal comfort across different urban climates.
- (5)
- Exploring Additional Influencing Factors: While this study considers various environmental and individual factors affecting outdoor thermal comfort, future research should explore other potential influences, such as cultural differences and individual health conditions. These factors could significantly affect perceptions of thermal comfort. A deeper investigation into these elements would contribute to refining thermal comfort assessment methods [79,80].
5. Conclusions
- (1)
- Among the ML models utilized in this study, LightGBM and CatBoost demonstrated particularly notable performance. LightGBM exhibited high predictive accuracy for TCV and TA, while CatBoost excelled in predicting TSV and HSV.
- (2)
- SHAP analysis revealed that the key factors influencing outdoor thermal comfort at tram stations primarily include RH, Ta, Tmrt, and Clo, alongside gender, age, BMI, and SOP20. Notably, the significance of physical parameters surpassed that of physiological and behavioral parameters.
- (3)
- In terms of predictive accuracy in machine learning models, the accuracy of binary classification models is significantly higher than that of multi-class classification models.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Literature | Predictive Index | Data Source | Influencing Factors (Input Features) | Algorithms and Results |
---|---|---|---|---|
2015 [44] | TSV | HVAC artificial climate chamber | Clo, air turbulence, Ti, age, Ta, MET, Tmrt | SVM (76.7%) |
2016 [44] | TSV | HVAC artificial climate chamber, Chongqing | Ta, Tmrt, V, RH, MET, Clo | C-SVC (89%) |
2017 [45] | TP | office, Northern California | Device usage, Ti, Ta, HVAC system data, Clo | CT, GPC, GBM, SVM, regLR, RF (Median AUC = 0.71) |
2019 [37] | TCV | Park, Hong Kong, China | age, education level, income level, occupation, gender, health status, thermal sensitivity, Ta, Tg, V, RH, Sr, thermal sensation, humidity sensation, purpose of visit, Clo, perceived number of water space in the park, metabolic rate, perceived density of trees in the park | ANN (Summer average R value 0.821) |
2022 [20] | PET | Construction Site, Korea | Ta, RH, V, Tmrt, PET, Sr, season | DT, RF, XGB, AdaBoost, Bayesian Ridge (Different models perform differently on different datasets; DT consistently lowest, all greater than or equal to 80%) |
2024 [21] | TCV | Shenyang University Outdoor Space, China | length of stay, distance between hometown and residence, Clo, age, height, weight, RH, V, Ta, Tg | extreme gradient lifting (93.13%) gradient lifting RF neural network |
2024 [30] | TCV, TSV, TA | Xi’an Park around City, China | length of stay of park, emotion, Clo, age, BMI, RH, V, Ta, Tmrt, SVF | KNN, MLR, OPM, DT, RF, SVM, XGB, LightGBM, CatBoost (65.49%) |
Measurement Point Name | Platform Type | Platform Image |
---|---|---|
Xiankeng | ground-level side platforms | |
Civic Square | ground-level separated platforms | |
Convention Center | ground-level separated platforms | |
Juntai Road | ground-level side platforms | |
Lingtou | ground-level side platforms | |
Xinfeng Road | ground-level side platforms |
Time Interval | Temperature | Globe Temperature | Humidity |
---|---|---|---|
30 min | 0.8 °C | 0.9 °C | 8% |
15 min | 0.6 °C | 0.5 °C | 4% |
5 min | 0.3 °C | 0.2 °C | 2% |
3 min | 0.2 °C | 0.2 °C | 2% |
Parameters | Instruments | Accuracy |
---|---|---|
Black globe temperature | TR-102 black globe temperature meter (Extech Instruments, Inc., Hudson, NH, USA) | ±0.2 °C |
Air temperature Relative humidity | TR-72Ui temperature and humidity meter (Extech Instruments, Inc.) | ±0.3 °C ±5% |
Air velocity | HD 2303.0 omni-directional anemometer (Extech Instruments, Inc.) | ±0.02 m/s (0–0.99 m/s) ±0.1 m/s (1–5 m/s) |
Variable | Min | Max | Mean | Std |
---|---|---|---|---|
Ta (°C) | 29.20 | 38.79 | 34.13 | 2.65 |
Tmrt (°C) | 28.45 | 39.05 | 33.90 | 2.87 |
RH (%) | 42.50 | 69.05 | 57.52 | 7.03 |
V (m/s) | 0.02 | 1.90 | 0.72 | 0.47 |
Gender | Number | Age (Years) | Height (cm) | Weight (kg) |
---|---|---|---|---|
Male | 354 | 41.5 ± 27.50 | 165 ± 9.5 | 65 ± 12.9 |
Female | 432 | 39.5 ± 23.50 | 158 ± 8.5 | 58 ± 9.3 |
Scale Points | Reference Standard | ||
---|---|---|---|
TSV | 7 points | −3: very cold; −2: cold; −1: cool; 0: neutral; 1: warm; 2: hot; 3: very hot | ASHRAE 55-2020 |
TA | 5 points | −2: very unacceptable; −1: unacceptable; 0: neutral; 1: acceptable; 2: very acceptable | |
TCV | 7 points | −3: very uncomfortable; −2: uncomfortable; −1: slightly uncomfortable; 0: neutral; 1: slightly comfortable; 2: comfortable; 3: very comfortable | |
HSV | 7 points | −3: very dry; −2: dry; −1: slightly dry; 0: neutral; 1: slightly humid; 2: humid; 3: very humid | ASHRAE standard 55-2020 in conjunction with [52] |
Model | Key Advantage | Applicable Scenarios |
---|---|---|
XGB | Efficiency (optimized algorithms, parallel processing) and high accuracy (gradient boosting, regularization) | Complex predictive tasks requiring high precision and flexibility |
LightGBM | Speed (histogram-based algorithm) and memory efficiency (reduces computation time and memory usage) | Large-scale data scenarios, memory-constrained environments |
RF | Resistance to overfitting (through Bagging and random feature selection) and robustness (handles noise and missing data) | Suitable for various types of data, especially noisy or missing data |
CatBoost | Handling categorical features and training speed (ordered target encoding, symmetric tree structure) | Complex data processing, especially for data with categorical features |
KNN | Simplicity and intuitiveness, easy to understand and implement | Small datasets and beginner-level tasks, problems with well-distributed feature spaces |
Types of ML | Hyperparameter Types |
---|---|
XGB | learning Rate; max_depth;n_estimators; booster; min_child_weight; subsample; colsample_bytree; colsample_bylevel; reg_lambda; reg_alpha; gamma |
LightGBM | n_estimators; learning_rate; max_depth; min_samples_split; min_samples_leaf; max_features; subsample |
CatBoost | Iterations; depth; learning_rate; l2_leaf_reg; border_count |
RF | max_depth; min_samples_leaf; min_samples_split; n_estimators; bootstrap; oob_score; class_weight; max_samples; max features |
KNN | n_neighbors; weights; p; algorithm; leaf_size; metric |
Algorithm | Macro-P | Macro-R | Macro-F1 | Macro-P | Macro-R | Macro-F1 |
---|---|---|---|---|---|---|
Default | Model + BO | |||||
XGB | 80.66 | 80.84 | 80.37 | 83.37 | 83.84 | 83.27 |
LightGBM | 78.95 | 79.67 | 78.78 | 85.22 | 85.45 | 84.90 |
CatBoost | 83.06 | 83.56 | 82.97 | 84.83 | 85.07 | 84.55 |
RF | 78.99 | 79.74 | 78.87 | 79.41 | 80.12 | 79.28 |
KNN | 69.50 | 69.99 | 67.85 | 73.56 | 74.13 | 72.63 |
Algorithm | Macro-P | Macro-R | Macro-F1 | Macro-P | Macro-R | Macro-F1 |
---|---|---|---|---|---|---|
Default | Model + BO | |||||
XGB | 87.83 | 80.65 | 83.35 | 88.65 | 81.19 | 82.99 |
LightGBM | 88.00 | 76.68 | 80.22 | 88.56 | 81.35 | 83.31 |
CatBoost | 87.78 | 80.76 | 83.33 | 88.69 | 81.61 | 83.93 |
RF | 86.39 | 79.38 | 81.53 | 87.75 | 80.70 | 82.88 |
KNN | 73.24 | 67.74 | 73.57 | 77.97 | 72.07 | 75.42 |
Algorithm | Macro-P | Macro-R | Macro-F1 | Macro-P | Macro-R | Macro-F1 |
---|---|---|---|---|---|---|
Default | Model + BO | |||||
XGB | 92.67 | 93.25 | 92.83 | 93.18 | 93.67 | 92.95 |
LightGBM | 91.74 | 92.30 | 91.92 | 94.22 | 94.26 | 94.20 |
CatBoost | 92.26 | 93.02 | 92.42 | 93.66 | 94.15 | 93.80 |
RF | 89.58 | 90.98 | 89.91 | 92.82 | 98.96 | 92.88 |
KNN | 89.32 | 90.89 | 89.03 | 91.78 | 92.46 | 91.61 |
Algorithm | Macro-P | Macro-R | Macro-F1 | Macro-P | Macro-R | Macro-F1 |
---|---|---|---|---|---|---|
Default | Model + BO | |||||
XGB | 70.77 | 72.59 | 71.49 | 74.48 | 76.66 | 75.39 |
LightGBM | 72.11 | 74.63 | 73.11 | 76.27 | 77.62 | 76.69 |
CatBoost | 73.38 | 75.34 | 74.06 | 76.63 | 78.31 | 77.24 |
RF | 69.92 | 73.84 | 71.23 | 70.17 | 73.87 | 71.25 |
KNN | 59.94 | 68.11 | 62.46 | 63.50 | 70.99 | 65.81 |
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Chen, X.; Zhao, H.; Wang, B.; Xia, B. Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning. Buildings 2025, 15, 865. https://doi.org/10.3390/buildings15060865
Chen X, Zhao H, Wang B, Xia B. Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning. Buildings. 2025; 15(6):865. https://doi.org/10.3390/buildings15060865
Chicago/Turabian StyleChen, Xin, Huanchen Zhao, Beini Wang, and Bo Xia. 2025. "Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning" Buildings 15, no. 6: 865. https://doi.org/10.3390/buildings15060865
APA StyleChen, X., Zhao, H., Wang, B., & Xia, B. (2025). Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning. Buildings, 15(6), 865. https://doi.org/10.3390/buildings15060865